Cnn

Overview Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are neural architectures built for different data shapes: CNNs slide convolutional filters across a spatial grid to detect local patterns, while RNNs pass a recurrent hidden state across time steps to model sequential dependencies. Choosing between them (or their modern successors) depends on whether your data’s structure is spatial, temporal, or both. Comparison Diagram CNNConvolution over a spatial gridconvolveFilter weights shared across all positionsCaptures local spatial patternsBest for grid-structured data (images)RNNRecurrence over a sequenceh1h2h3x1x2x3y1y2y3Hidden state carries contextforward through the sequenceBest for sequential/time-ordered data Comparison Table Aspect CNN RNN Input data shape Fixed-size spatial grid (2D/3D tensors like images) Variable-length ordered sequence (text, time series, audio) Core operation Convolution: a filter slides over local receptive fields Recurrence: hidden state updated step-by-step from previous state plus current input Weight sharing Same filter weights reused across all spatial positions Same weight matrices reused across all time steps Context captured Local spatial neighborhoods, expanded via depth/pooling Temporal history accumulated in the hidden state over prior steps Order sensitivity Largely order-invariant beyond local structure; pooling discards exact position Strictly order-dependent; reordering the sequence changes the output Training parallelization Highly parallelizable across positions, channels, and layers Inherently sequential; each step waits on the previous hidden state Common failure mode Limited receptive field unless network is deep or uses dilation Vanishing/exploding gradients over long sequences Typical applications Image classification, object detection, segmentation Language modeling, time-series forecasting, speech recognition Key Differences CNNs assume spatially local structure and share filter weights across the whole input; RNNs share weights across time steps instead. CNN layers process all positions in parallel, while RNNs are sequential by construction since each step needs the prior hidden state. RNNs suffer from vanishing gradients over long sequences; CNNs sidestep this but need deeper stacks to grow their receptive field. Shuffling pixels barely changes what a CNN detects, but reordering a sequence fed to an RNN changes the output entirely, since RNNs are order-sensitive. CNNs expect fixed-size grid inputs, whereas RNNs natively handle variable-length sequences. When to Use Each CNN ...